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When I sat down to think about the very first piece to write for CoppettHill.com, Customer Lifetime Value was an obvious choice, as it sits at the centre of so many topics that I want to cover. In fact, most conversations about growth and marketing investment come back to the value of an individual customer or different types of customer – whether that is defining your Ideal Customer Profile (ICP), choosing how much to spend on marketing, or considering how to develop your proposition for the benefit of customers. In simple terms though, the best use of Customer Lifetime Value in my experience is to determine what a business should rationally be prepared to ‘pay’ to acquire a customer. In today’s environment of pressure on marketing & sales budgets and an emphasis on customer retention, this feels like an even more important question to tackle, so let’s jump in. What is Customer Lifetime Value? Customer Lifetime Value is the profit contributed by a unique customer over their lifetime transacting with a business. Or to put it the other way round, the profit a business would lose if a unique customer had never existed. We’ll come back to what ‘lifetime’ means in practice later. This concept has its history in database marketing - think catalogue retailers and credit card providers, those businesses where it was easiest to build a single view of customer transactional behaviour over time in days before the internet. The term ‘Customer Lifetime Value’ was used at least as early as 1988 in ‘Database Marketing’ by Merlin Stone, and first featured in the Havard Business Review in 1989. What I really appreciate about the concept of Customer Lifetime Value is that it has stood the test of time – I recently re-read this article from 1998 (the year Google was founded) it still rings true today. This makes it one of the very few marketing or growth concepts to have made the shift from analogue to digital marketing largely unscathed. I’d argue that it has become even more relevant as marketers have become more data-driven over the past 20 years. How to use Customer Lifetime Value? There are four main uses of Customer Lifetime Value that I see, starting with the most frequent: To calculate ROI on marketing spend, when combined with Cost Per Acquisition (CPA) data; To compare between different customer segments (which can tell you either attributes of customers that make them more attractive to your business and/or groups for whom your proposition is a better fit); To measure the impact of historical business changes over time – seeing how Customer Lifetime Value changed; and To model the potential impact of future business changes – to combine different assumptions and forecast customer profitability scenarios. Whilst LTV is a great concept to embed in both daily decision making and big strategic decisions, I don’t think it is well suited to routine monthly Management/Board reporting. As a lagging, historical measure it is unlikely to move by much month to month, so it is better suited for an annual strategy day, or to be operationalised into marketing decisions e.g. Paid Search bidding for different customer segments or partnership commercial models. How to calculate Customer Lifetime Value? To calculate Customer Lifetime Value, you need to consider all revenues associated with a unique customer, then remove all direct costs, a fair share of variable operating costs, and any reacquisition costs for subsequent transactions. The specifics here will vary by business model and for each customer, but to give some more examples: Revenue – this should include both the main transactional revenue from a customer, but also any ancilliaries or one-time income, for example cancellation insurance added to a holiday booking, or one-off implementation fees associated with a SaaS subscription. Don’t forget to also allow for discounts offered to customers – only count the true revenue received. Direct costs – the best way to think about this is your gross margin – either the costs of physical goods or services, as well as staff costs allocated to a specific transaction. Don’t forget to also include the costs associated with ancillaries or one-time income, as well as things like bank fees/payment processing, logistics, insurance, returns etc. Fair share of variable operating costs – these are costs that you might not allocate to specific customers on a day-to-day basis, but which broadly correlate to the number of customers you are serving – for example Customer Service or Support teams. This is the one where there is normally the most debate about what to include in an LTV analysis. Reacquisition costs – some repeat transactions will have additional marketing or sales costs, for example the staff cost to secure a renewal in a SaaS business, or a price comparison website commission fee for an insurer. Getting hold of the data put this analysis together takes time, in my experience it is normally easier in B2C than B2B businesses as you will typically already have access to customer-level revenue data. You may have to be creative - I’ve had to use invoice level data from finance systems or stitch customer data together from multiple sources - but I've always managed to find the right information in the end. Some of the inputs into a Customer Lifetime Value calculation will be at unique customer level (normally revenue data), for others you will need to make assumptions for segments of customers or for everyone (normally cost data). There are many tools available that claim to have some version of Customer Lifetime Value analysis available ‘out of the box’, but I prefer to start by calculating it directly. There will always be limits to analytical capabilities with a set of pre-configured reports/dashboards, and most will make at least one of the common mistakes I talk about later on. What does ‘Lifetime’ really mean? Every customer’s lifetime with your business will be different – and just because they may have stopped transacting with you for now, doesn’t mean they will never come back. To get round this dilemma, I use the concept of a ‘lifetime window’ in my Customer Lifetime Value analysis. This is a standard period of time, often 3 or 5 years, from the first transaction with a customer. It allows for standardised analysis and comparison between unique customers or customer segments. Determining which time period to use for the ‘lifetime window’ isn’t an exact science, but is a trade-off between the length of the window and how many customers will be eligible for the analysis. If we set a 5 year ‘lifetime window’, our historical analysis won’t include customers acquired less than 5 years ago. You should only decide this once you’ve assembled your historical data – and is why should always build the longest time-series of data as possible, within reason. This makes historical Customer Lifetime Value analysis particularly challenging for new businesses. In these situations I’ve used a much shorter window, sometimes 12 months or less. You may have seen examples of Customer Lifetime Value analysis which use a method of dividing annual revenue by an expected annual churn rate, sometimes also with a discount factor. Whilst this often produces very high estimates of Customer Lifetime Value (great when talking to potential investors), I’d always stick to using actual, historical behaviour if you are trying to make strategic choices. The pattern of revenue will vary based on your business model, or potentially within your business – is your product/service an annual purchase, a frequent purchase or a subscription? Some businesses may even only transact once with the vast majority of customers (think divorce lawyers or funeral directors!). Using a ‘lifetime window’ will help to standardise any analysis. What is a good Customer Lifetime Value? The answer is clearly ‘it depends’. This will entirely depend on your business, and I wouldn’t advocate using Customer Lifetime Value as a benchmark metric in isolation. There are some obvious rules of thumb however – within a niche of comparable propositions, you will see higher lifetime value for those businesses with (i) better margins, (ii) better repeat rates, and (iii) better ability to up-sell/cross-sell to customers. What segments should I consider when analysing Customer Lifetime Value? One of the most powerful questions you can answer with Customer Lifetime Value analysis is “Who are our most valuable customers?”. To answer this, you can analyse the relative LTV of different segments based on different customer-level dimensions. These could be ‘attributes’ such as age, location or industry vertical; or ‘behavioural’ such as what the customer purchased first or which marketing channel they came from. This is a process of elimination, test many different dimensions and narrow down to the ones that make a difference. When you find the combination of dimensions that allows you to create a segmentation that balances the best spread of LTV vs equal distribution of customers, you can start to operationalise this. This could be with just one characteristic, for example risk type in an insurance business, or a combination of 2 or more dimensions. It is best to not over-complicate your segmentation at first as it will be harder for your stakeholders to understand and then hard to operationalise. Make sure that you always pay attention to any outliers in your analysis - very low or loss making customers, or super profitable customers. These can lead you either to great insights or bugs in your analysis that need fixing, and sometimes both! What are the common mistakes with using Customer Lifetime Value? This isn’t an exhaustive list, but there here are five of the most common mistakes I’ve seen when reviewing Customer Lifetime Value metrics: Only considering revenue – the most common mistake, where LTV is stated at revenue rather than profit level. This can lead to poor decision making and ultimately value erosion. Ignoring reacquisition costs – repeat purchases from customers will often carry additional costs, sometimes very significant ones, for example in businesses which spend significantly on advertising. Did the customer repeat purchase because they were loyal or because they saw your advert again when searching generically online? Not factoring in customer service costs – in some business models, there is a significant amount of staff cost required to service existing customers, which can be ignored in LTV analysis. There is some subjectivity on where to draw the line, but a share of the cost of large teams such as Customer Service or Support should be factored into your analysis Ignoring changes in a business over the historical period e.g. the introduction of new revenue streams or a major change in pricing. This can complicate analysis, but if you want to use this metric to make choices today about the future, you should make adjustments to historical data to best reflect the future value of customers you have acquired today. In practice that might mean re-stating historical revenue for some customers. Using Customer Lifetime Value to make decisions in isolation. You aren’t seeing the whole picture if you do this – for example you could have a great picture on “Who are our most valuable customers”, but they could represent only a tiny proportion of your potentially addressable market, or have a very low conversion rate vs other customer segments. Make sure to combine LTV analysis with market size and conversion rate data. How to increase Customer Lifetime Value? I’ve written a separate piece about this which you can find here. If you’d like to discuss how you can better understand and use Customer Lifetime Value in your business, please Contact Me. All views expressed in this post are the author's own and should not be relied upon for any reason. Clearly.

It’s the obvious follow-on from any analysis of Customer Lifetime Value (CLV or LTV) – ‘that’s great, but how can we increase LTV’? It’s actually a great question, as it will force you to think about growth from a customer perspective – and in my experience has led to some of the highest quality discussions around the board table. It is a key part of establishing your own marketing flywheel. There are of course many different ways to increase Customer Lifetime Value, but I wanted to offer my top five. These are inevitably quite generic, but think of these as conversation starters for you to adapt to your own business. As with most choices about strategic growth, you won’t be able to tackle many of them at once, so be sure to prioritise based on potential impact on LTV and expected effort. Five ways to increase Customer Lifetime Value 1. Change the customer mix: if you’ve analysed LTV for different segments of your customer base, you will have some insights about which types of customers are worth more to your business. This segmentation might be based on ‘attributes’ such as age, location or industry vertical; or ‘behaviours’ such as what the customer purchased first or which marketing channel they came from. You can then start to adapt your go-to-market efforts to attract more of the higher LTV efforts – by changing your marketing mix, messaging or perhaps offering discounts. For example, I’ve worked with a travel business which saw that customers who booked larger properties for their first booking had a higher LTV, as they would typically continue to book larger properties on subsequent trips. They started to spend more on search terms which attracted extended family/group bookings as a result of this insight. 2. Develop up-sell and cross-sell opportunities: consider how to develop additional revenue opportunities with your customers – could be ancillary add-ons like premium delivery and insurance/cancellation products in the B2C world, or enhanced service levels and additional features in SaaS models. Often these are also higher margin than the core product or service offering so have disproportionate impact on Customer Lifetime Value. 3. Pricing optimisation: as the saying goes; ‘some of your customers would have paid more, the challenge is working out which ones’. Although it is getting more attention in the current macro-economic environment, in my experience pricing is one of the most under-used value creation levers. When it comes to increasing Customer Lifetime Value, pricing analysis can be used to design different packages for customer use cases, or to incentivise repeat purchasing behaviour through discounting. You should also consider the role of regular price increases in your business. It is also worth examining the highest value customers that your LTV analysis identifies, as this can often lead to opportunities for different proposition/pricing models – for example business customers using a B2C platform. 4. Reduce cost to serve: the process of allocating both direct costs and a fair share of variable costs to unique customers as part of LTV analysis can offer valuable insights about the efficiency of how you deliver your proposition. For example, I’ve worked with a SaaS business that saw a disproportionate number of support cases (and resultant costs) from one part of their product suite. By changing how customers are onboarded, and improving the quality of support documentation, they were able to reduce support calls and increase LTV. 5. Improve customer retention or repeat purchasing behaviour: whilst some of the ideas above might also improve customer satisfaction and retention, I’ve always found it incredibly helpful to focus directly on the reasons why customers churn or fail to repeat. This exercise requires a lot of primary research with customers, analysing reviews and listening back to support calls. One shortcut is that in my experience, Net Promoter Score is (unsurprisingly) well correlated with propensity to repeat. For example in a travel business I worked with, customers rating their likelihood to recommend a business as 9 or 10 were 3x as likely to repeat book than those rating 6 or below. This insight provided both motivation to focus on the drivers of dissatisfaction but also allowed the Management team to quantify the impact of customer service improvements on Customer Lifetime Value. And finally… Don’t forget that increasing Customer Lifetime Value might not be the best place to spend your time and money right now. The most obvious growth lever to pull next might just be more customers through better conversion. There can also be negative impacts on conversion of changes to LTV, for example imagine what would happen if you doubled prices. If you’d like to discuss how you can increase Customer Lifetime Value in your business, please Contact Me. All views expressed in this post are the author's own and should not be relied upon for any reason. Clearly.

The concept of Cost Per Acquisition (CPA) or Customer Acquisition Cost (CAC) seems incredibly simple – but there is often more to it than meets the eye. What is the difference between CPA and CAC? Nothing! These terms are used interchangeably. For simplicity I’m going to just talk about Cost Per Acquisition (CPA). What is CPA? We can define Cost Per Acquisition as the total costs associated with customer acquisition divided by the number of new customers in any given time period. One important consideration is whether you are calculating for all transactions or customers, or differentiating between costs associated with first time customer acquisition and reacquisition/retention marketing. This will be more important in some business models (eg occasionally repeated purchases like travel or clothing) than others (like subscription products). Splitting costs in this way can be tricky but is important if you want to compare your (new customer) CPA with Customer Lifetime Value. What to include when calculating CPA Each business I’ve worked with has calculated CPA in a different way – and it is key to understand what is included/excluded before drawing any conclusions from trends or comparisons. In general, you should include all costs associated with customer acquisition, which will vary based on your business model but might include: Media costs – both digital (like Paid Search or Paid Social spend) and traditional media (like TV or events). Agency costs – all of the various agencies you may work with from digital agencies, creative agencies through to PR and translation. Technology – you should be factoring in the costs of technology that plays a role in your customer journey – your website, ecommerce platform, email or marketing automation tool, CRM, conversion rate optimisation tool, ad serving, bidding and analytics platforms. Partners / affiliates – this could range from channel partners, marketplaces, intermediaries through to influencers and ambassadors. Content creation – the cost of producing content – copywriters, imagery, video production Personnel – the fully loaded costs of your marketing and sales teams, including the value of any commission based incentives. An important aspect of CPA is that it is an aggregate measure, typically analysed at a segment level which corresponds with how you make decisions on marketing costs, for example for a particular product/service or single marketing channel. Revenue allocation between channels will typically come from your attribution model. You will likely have to make some assumptions about how to allocate costs between these segments, but using common sense and following a simple volume or revenue based allocation will normally suffice. It isn’t particularly helpful to calculate CPA for a specific customer as this look artificially low as it ignores the ‘wasted’ spend on customers who didn’t convert, but which is an unavoidable consequence marketing activity. Some costs make more sense to factor into Customer Lifetime Value than CPA as they are more related to the transaction or product/service than the acquisition itself, for example bank fees. As a rule of thumb, only include costs associated with getting to the point of transaction in your CPA – anything directly associated with the transaction should be captured in Customer Lifetime Value. One of the most powerful uses of your CPA metrics is the LTV : CPA ratio, which I’ll cover in a separate article soon. How to reduce Cost Per Acquisition (CPA) / Customer Acquisition Cost (CAC) There are three levers to consider which can help you to reduce CPA: Change the mix: any analysis of marketing channel performance will show you where you have the highest CPA, potentially unprofitable at a customer level. Reducing your spend in this area is the simplest way to reduce overall CPA. Lower the cost per lead (or click): examine where your leads / website traffic is coming from and how you might be able to increase cost efficiency at a channel level. Common tactics I’ve used are increasing your Quality Score in Google Adwords, or renegotiating partner/affiliate agreements. Increase your conversion rate: often the most effective lever to reduce CPA, use a test and learn approach to improve each stage of your customer journey, both online and offline. This process is often best informed by conducting primary research with your customers (and ideally lost prospects) to understand where they found points of friction in your customer journey. One of the most common tactics is to increase the speed with which you respond to inbound enquiries, which I’ve always found to be highly correlated with conversion rate. Lower your cost to convert: this is particularly relevant if you have sales teams, for example shortening your sales cycle, reducing the number of interactions or using automation to encourage more self-service. For example, I’ve worked with an insurance business which progressively built out their online journeys to reduce the number of telephone calls and consequently reduced cost to convert. Although most businesses will be able to use these levers to reduce CPA, most Management teams will care just as much (or even more) about driving growth. It is very difficult to pursue both growth and marketing efficiency, even though I’ve seen many business plans promising both. The most successful businesses I’ve worked with have been able to balance out efforts to reduce CPA with driving growth – consider carefully what assumptions you use in your business plan. The reason for this is that as you grow your marketing budget, you will typically see diminishing returns – in other words, the more leads you try to drive, the higher the cost per lead. There are two drivers of this: Many paid channels such as Google Adwords operate a bidding model, to secure more traffic you need to place a higher bid. If you’ve fully optimised your CPA, to grow leads you will need to start looking at more expensive ways of generating traffic or leads, e.g. starting to run paid digital marketing if you’re not already doing so, launching in countries with lower conversion rates. The most successful businesses I’ve worked with have been able to balance out efforts to reduce CPA with driving growth – consider carefully what assumptions you use in your business plan. Cost Per Acquisition may seem like a simple metric, but spending time analysing how it is calculated and how it can be optimised is a key part of growth acceleration. It is a key part of creating your own marketing flywheel. If you’d like to discuss how you can better understand and use Cost Per Acquistion in your business, please Contact Me. All views expressed in this post are the author's own and should not be relied upon for any reason. Clearly.

The ratio of Customer Lifetime Value (LTV) to Cost Per Acquisition (CPA or CAC) is one of the most important commercial metrics for any business. It represents the fundamental unit economics of customer acquisition and how efficiently a business is able to grow. In simple terms it is the return on investment (ROI) of marketing spend. In spite of this, in my experience most Management teams of small and mid-market businesses have never calculated it. You can understand why, as there is often a lot of setup analysis involved. As a task, this almost always falls into the 'important but not urgent' category and struggles to get to the top of your to-do list. Instead, marketing efficiency is calculated using CPA on its own, or perhaps by looking at marketing spend as a % of revenue. However, once you step in the investment world, the LTV:CPA ratio is favoured by bankers and private equity investors, often having a prominent position in Management presentations and sales documents. This is because it is both easy to understand for non-marketers and highly comparable both within and between market segments. For investors looking to assess future growth potential, a lot is inferred from the LTV:CPA ratio, making it important for all Management teams to understand well ahead of any investment process. Needless to say, the first step is to ensure that both input metrics are calculated comprehensively, I’ve written previously about how to do this for Customer Lifetime Value and Cost Per Acquisition. For any investor, I would also recommend probing the basis of each metric rather than taking a quoted LTV:CPA ratio at face value. In my experience, when this ratio is calculated as part of an investment process, there are normally some shortcuts taken which unsurprisingly can result in an overstated ratio (for example, using considering customer revenue rather than customer profitability for LTV). I've compared some real-world LTV to CPA ratio examples in another post to help you do this. The LTV:CPA ratio will tell you the ROI of marketing spend in the time period over which Customer Lifetime Value is calculated, normally 3 or 5 years. One alternative metric which uses the same inputs is the Payback Period, normally quoted as the number of months it takes for a customer to generate profit equal to the initial CPA. It is helpful to consider both metrics so that you understand the ‘J-Curve’ of customer acquisition - how long a business will be ‘out of pocket’ at both profit and cashflow levels after spending to acquire a customer. Whilst most Management teams are happy to invest to accelerate growth, there is often a constraint on cashflow or a minimal level of in-year profitability required. What is a good LTV:CPA ratio? As described in my introduction to Cost Per Acquisition, CPA is a metric which will typically increase as a business seeks to drive more demand (i.e. you will see diminishing returns) - you could think of it like a supply curve. This means that the LTV:CPA ratio will narrow as a business grows faster, all things being equal. We therefore need to consider the LTV:CPA ratio in combination with growth rate. For businesses experiencing good double digit annual growth, say 20-50%, I’ve seen 5-year LTV:CPA ratios mostly in the 3:1 to 5:1 range. If the ratio is above this level, there is normally potential to accelerate growth by investing more in marketing. If the ratio is at the bottom end of this range or even narrower, this is often an indication of a very competitive market (e.g. travel or personal lines insurance), and/or an early stage business with lots of scope to optimise conversion and Customer Lifetime Value. This could also indicate some inefficiency in marketing spend which could be addressed to improve the LTV:CPA ratio. How to use the LTV:CPA ratio The LTV:CPA ratio can be set as a target by Management teams, and used to optimise marketing spend both between and within channels. It allows boards and Management teams to trade off short-term vs long-term profitability by adjusting the level of marketing investment and consequent growth rate (I’m going to talk about this in more depth in an upcoming piece). Using the ratio in this way is a key indicator that your marketing function is operating as a profit centre rather than a cost centre. For example, if a business is working to a target LTV:CPA ratio of 4:1, the marketing and sales teams can optimise their activities to this level of ROI, with the growth rate will change as a consequence of these changes. One very important watch-out is that this ROI ratio should be implemented as a minimum ratio rather than an average. Using an average can mask a lot of inefficient marketing spend. Over time you can also apply your ROI target with increasing granularity. For example, if you are running Paid Search (PPC) activity, you might start using the target at a campaign level, then move on to ad group and ultimately at keyword level. It is also important to make sure the input Customer Lifetime Value is frequently updated for any changes in business model or customer behaviour – for example if additional marketing investment starts to generate customers with a lower LTV this would be an important consideration. If you’d like to discuss how you can better understand and use the LTV:CPA ratio in your business, please Contact Me. All views expressed in this post are the author's own and should not be relied upon for any reason. Clearly.

It goes without saying that being able to efficiently acquire new customers is integral to long-term profitable growth. But what if it your capability to acquire new customers became a source of sustainable competitive advantage in the same way that superior scale, proprietary data, long term customer relationships or hard to get accreditations are? Sustainable competitive advantage is typically defined as the ability to outperform competitors in a way that is very hard for competitors to replicate. In my experience, businesses in a variety of markets have been able to create sustainable competitive advantage through leveraging the marketing flywheel effect. Building such an advantage is key part of your marketing function operating as a profit centre rather than a cost centre. What is the marketing flywheel To turn your customer acquisition efforts into a source of sustainable competitive advantage, you need to a virtuous cycle of customer acquisition, also known as a marketing flywheel because of its potential multiplier effect on growth. There are four components to this flywheel, which follow the customer lifecycle: 1. Generate demand from wide variety of sources 2. Optimise cost and quality of demand 3. Maximise conversion / yield of demand 4. Understand and maximise customer lifetime value To create the virtuous cycle, or flywheel effect, you will need to drive a process of continuous improvement in each of these four areas. The result is that you will be able to be able to spend more per unit of demand that your competitors because you have confidence in making a higher return on this marketing investment. This investment might be in the form of media spend, partner funding or potential even discounts. For example, a foodservice concession operator could be prepared to offer the best terms to a landlord as you have confidence in making a higher yield than any of your competitors. As a business starts to develop this flywheel effect, they benefit from seeing even more data to optimise against and reinforce the initial advantage. Think of this as like an experience curve – the more you do something, the easier and better you do it. When I started leading marketing efforts in the car rental sector in 2014, my big competitor was part of Priceline Group, and they were consistently appearing in the top position in paid search. To outbid them, I realised I would have to increase my bids by at least 3x – which would have been deeply unprofitable at that point. As I started to learn more, I realised that my competitor was applying learnings from their Priceline stable-mate, Booking.com – one of the most well known examples of the marketing flywheel, using constant experimentation (sometimes more than 1,000 experiments at once) to drive improvements to conversion and yield. Their approach has been documented in a great HBS case I’d recommend reading, or a helpful summary here. I spent the next three years creating our own marketing flywheel to close the gap and allowing us to compete effectively. How to create a marketing flywheel in your business There are many ways you can start to drive optimisations in each of the four stages of the marketing flywheel, and I’ve suggested a few ideas to get you started. As the Booking.com example highlights, what matters is that you test many different ideas, take the learnings and evolve continuously. Make sure that when designing a test, you will come up with a definitive answer – ‘this maybe works’ is an unhelpful outcome. 1. Generate demand from wide variety of sources Finding the most effective channels to reach your target audience – in your particular market there will be many different options for how to reach your prospective customers – digital marketing, partnerships, events, above the line, outbound lead generation. Understanding your headroom for growth in different channels is an important input here, for example completing a Search Headroom analysis in organic search. Adopt a systematic approach to testing each channel – at enough scale that you will understand the incremental impact on your marketing outcomes. 2. Optimise cost and quality of demand I’ve talked about this subject in the context of Cost Per Acquisition, which I would suggest reading. Within each marketing channel, test all of the variables you can control – the targeting, creatives, copy, and landing pages for digital marketing; the content and format of events, or the commercial model in partnerships. Improve the accuracy of your measurement and attribution – for example I’ve worked with an online travel business that generated significant competitive advantage from having the best mobile device attribution model. 3. Maximise conversion / yield of demand Examine every aspect of your marketing journey from the customer perspective to remove frictions and reinforce your value proposition. Do this for both the online and offline parts of your journey, e.g. consider for a SaaS business consider whether a self-serve or assisted sales motion is most effective. I’ve always found mystery shopping your own product or service produces a long list of potential improvements. Pricing optimisation – review both your approach to packaging and headline pricing. I’m going to cover this in more depth in a future article. 4. Understand and maximise customer lifetime value I’ve talked previously about how to calculate and improve Customer Lifetime Value. The key is that you need to have enough confidence to use your calculation of LTV as the basis for your decisions about marketing investment i.e. using LTV:CPA ROI. If you miss this critical step, you are unlikely to be able to create that competitive advantage as someone else in your market will probably be thinking this way. You will likely uncover which customer segments offer the right balance of both superior lifetime value and ability to target in large numbers through your marketing efforts. If you are wondering where to start developing the marketing flywheel in your business, you could ask yourself: · If you cleared your diary for tomorrow, what would you spend your time on? · What part of the flywheel do you know the least about in your own business? · Where have you spent the least time to date? · Where is the biggest bottleneck in your customer acquisition efforts? · How do you benchmark vs your competitors in each of the four stages? It is important to remember that even if you create competitive advantage through superior customer acquisition, you should never get complacent. Your know-how will leave each time a member of your marketing team moves onto a new role in a different, competing organisation – as evidenced by the number of travel start-ups now led by Booking.com alumni. ‘Sustainable’ advantage does not mean ‘permanent’. If you’d like to discuss how you can create a marketing flywheel in your business, please Contact Me. All views expressed in this post are the author's own and should not be relied upon for any reason. Clearly.

What is Marketing Attribution? Marketing attribution is the process of determining how different marketing activities contribute to customer acquisition and retention. It plays a critical role in measuring the effectiveness of marketing efforts and helps businesses to optimise their budgets and strategies for maximum return on investment (ROI) and minimum waste. Purchase journeys starting today are more likely than not to start online, whether they are B2C or B2B (the stats are from 2018 and I think it safe to assume that the %s would be even higher today). They are also incredibly complex, with Google claiming that the average purchase journey has between 20 and 500 touchpoints. Regardless of the specific number of touchpoints, it is easy to understand how they can quickly accrue - customers interact with brands through a multitude of channels, such as social media, search engines, review sites, email campaigns and in person at stores or events. These interactions can occur across multiple devices and may span several days or even weeks. Furthermore, in complicated B2B purchases, multiple individuals within a purchaser’s organisation might be involved in the decision-making process over many months. As a result, a Chief Marketing Officer must understand the cause-and-effect relationship between their marketing initiatives and customer behaviour. Without a consistent approach to attribution, you can find every channel coming up with their own ‘measures of success’, often provided by media owners – which understandably tend to overstate performance. This is an important step in creating sustainable competitive advantage through adopting a ‘test and learn’ approach to customer acquisition, something I’ve talked about previously. It is also a key step in shifting from marketing operating as a cost centre to a profit centre, with the ability to measure and improve Return on Investment (ROI). How to create a Marketing Attribution model? The topic of attribution ‘modelling’ can provoke a host of reactions from marketers, often based on their own flavour of marketing – some think that attribution modelling systematically undervalues top-of-funnel brand marketing, others will obsess on the precise allocation of credit between different touchpoints to maximise the accuracy of their models. My take is that the clue is in the name, an attribution model is just a model – a tool that should help you make better commercial decisions. It will almost certainly be wrong in some respects – you are trying to get as close as you can to replicating real-world customer behaviour, but it is unrealistic to expect to achieve 100% accuracy. My main suggestion to anyone spending time on attribution is to not let perfect be the enemy of good enough – this is something you should iterate over time. One important qualifier before you invest meaningful time in analysing attribution – ask yourself whether you have a high enough volume of customers that you couldn’t just interview each customer to understand why they purchased (for example, if you only have 10 enterprise clients, this could be a better approach). Regardless of whether you go down the attribution modelling route, it is always interesting to ask your customers how they heard about you, but this isn’t a substitute for a robust attribution model. I follow four steps to build an attribution model, and I’ll talk through each in turn: Building a base dataset for each unique prospect journey; Applying a model to ‘attribute’ credit between each touchpoint in a unique prospect journey; Start making decisions based on your model and test if it increases ROI & growth; and Supplement your model with additional measurement techniques if needed e.g. incrementality testing and/or marketing mix (econometric) modelling. 1. How to build an attribution dataset? The cornerstone of your attribution model is to create a proprietary dataset of unique prospect journeys. Think of this as a data table where each row represents a touchpoint between a potential customer and your business, sorted in order from first to last. In the columns are a variety of details about each touchpoint for example a date-time stamp, the type of interaction, details of the device and browser for website visits, a unique identifier such as GCLID from Google Analytics, traffic source details and any flags related to your customer journey e.g., whether the individual logged into your website. The types of touchpoints you can include at an individual user / prospect level will vary based on your current marketing activities and your business model, but might include: Online interactions: Website visits Formfills and content downloads on platforms such as LinkedIn/Meta Email opens Mobile app downloads / sign-ins Display ad views Offline interactions: Outbound calls attempted/completed Inbound calls (for example using specialist call tracking software) Booked sales calls & demos Event attendance Partner referrals In-store purchases Some types of touchpoints are very difficult or impossible to track at an individual user/prospect level – and you may be better to assess their impact through a different approach such as incrementality testing, which I’ll talk about later in this post. I think it is incredibly valuable to build this dataset for yourself. Relying on third party tools can limit your ability to inspect the underlying data. Being able to explore this dataset for yourself is powerful – to prompt questions and to check that the results make sense. Relying on channel-level attribution exposes you to the challenge that media owners often overstate their own impact. Just try adding up the ‘conversions’ claimed by Google Ads, LinkedIn, and Meta for example – you will get a number that is often greater than your total actual conversions. I’m not going to get too technical in how you go about doing this as the specifics will depend on your business model and existing data stack – I’m happy to discuss if you want to contact me. However, the default I follow is to start with exporting data from Google Analytics for ecommerce / B2C businesses and combining marketing automation and CRM data for B2B business. The tools you need to do this e.g., csv downloads vs APIs, excel vs SQL/Python, will depend on the size of your business and nature of your customer journey. An important step for any advertiser is to be able to join marketing and customer journey data to sales data / conversions at a customer level. You will need to use a common key or unique identifier to do this (for example collecting Google Analytics Client ID – GCLID – at point of conversion into your own systems such as a CRM). This join allows you to understand the value of a specific conversion more precisely at a profit level (or even better customer lifetime value prediction). Now some of your touchpoints will contain personal identifiers such as an email address or perhaps your own unique identifier that allow you to join them together. Others will effectively be ‘anonymous’ so you will need to look for other ways to combine them. The main approach I use is to use GCLID which for most online visits will be persistent for any given device over time. This is placed in a cookie by GA so has been somewhat impacted by changes to cookie consent, but provided your Google Analytics is configured server-side will survive the upcoming deprecation of third-party cookies. This approach doesn’t help to join different devices that belong to a single user however – for this I will look for any combinations of GCLID with your own unique identifiers or personal information to ‘join’ two or more GCLIDs together. For example, I supported an ecommerce business to track GCLID from their email click-throughs and ‘my account’ visits, which had a high mix of mobile visits. By combining with their own unique identifiers, they could then join mobile and desktop sessions in the purchase journey. For B2B journeys where there are potentially many individuals involved, I will often use email domain to group individuals from the same prospect. You might also be able allocate individuals to accounts within your marketing automation platform and/or CRM system. When joining together touchpoints into journeys you will need to think about some rules such as – ‘how long should I allow between touchpoints before we are really looking at a new journey?’. The answer to this should be common sense based on your product/service and typical sales cycle – 28 days is too long for grocery delivery, too short for enterprise software and probably just right for high-value holidays. 2. How to define an attribution model? Now you’ve got your list of interactions for each journey, you need to decide how to allocate the ‘value’ of each conversion against the contributing touchpoints. There are a few standard models that you will probably have heard of: First touch/click – all the credit is allocated to very first touchpoint in a journey, a proxy for how the customer heard about you in the first place. To me this normally offers the right balance of common sense and simplicity. Last touch/click – all the credit is allocated to the very last touchpoint in a journey. The default for several tracking tools such as Google Analytics. Tends to overstate the value of both navigational channels such as branded Paid Search and affiliate channels such as discount sites. Decay & divide evenly – less common but basically credit is divided across multiple touchpoints, with decay rewarding the most recent touchpoints and divide evenly doing exactly what the name suggests. I think these are arbitrary and have never used them. ‘Data driven’- a term you will hear a lot, as the new GA4 will default to ‘data-driven’ attribution. This will mean something different in every context but beware any black box where the rules or model principles are not explained. I would personally steer clear of this unless you are able to get a very clear understanding of what any ‘data-driven’ model is trying to do. Which to choose? Well, the real answer is that you should test all of them to understand which most closely matches the true incremental impact of each aspect of your marketing activity. But that isn’t really a very practical approach! My advice is to start by keeping it as simple as possible. I’ve seen lots of people agonise over this decision and often 80% right is good enough. My default is first touch/click – it makes the most sense to me and will often skew attribution in a logical way towards upper funnel and generic marketing activity. In a recent example of a model that I built in the travel sector, non-brand Paid Search saw 60% more profit allocated to it with a first touch/click model compared to a last touch/click model. I should say that adopting a first touch/click model does not mean that the channels, content and creative that sit in the middle and the latter stages of the customer journey are not contributing – in fact they are normally critical to successful conversion. However – allocating significantly more of your budget to these activities is unlikely to attract brand new prospects to your business, notwithstanding the point I’ve made before about the role of continuous improvement throughout your customer journey. The right answer will also differ for each business – when I worked in the super competitive travel industry, I adopted a custom data-driven model where I was able to understand the underlying principles – but for many of the SMEs I worked with as an investor, this would have been massive overkill. 3. How to use an attribution model? So, you now have a model that gives you a view of marketing spend and ROI at a channel, campaign, and potentially keyword/creative level. The first step in using your new attribution model is introducing it anywhere you are making decisions about marketing spend – whether big picture strategic discussions around the board table, or granular bidding decisions in performance marketing channels such as Paid Search or LinkedIn. You should start to see whether it leads to (a) better quality discussions and (b) higher ROI and profitable growth. A good place to start is to focus on anywhere that your attribution model suggests is losing you money: whether that is whole channels, campaigns, or individual keywords with very low ROI. One thing I always look at is search terms which haven’t generated any revenue for a while e.g., the last 3/6/12 months. You can initially be more confident when using the results of your model in respect of digital, click-based media – you may need to supplement with some of the additional analysis I explain below for offline and impression-based media. You should adopt the same ‘test and learn’ approach with your attribution model as you would do with other aspects of your marketing and customer journey. Make some changes and monitor the results carefully. Did the results match what your attribution model suggested? Don’t be afraid to make changes if not. Finally, it is imperative to communicate to your stakeholders. Many times, I’ve seen a marketing team start to measure performance in a new way and a finance team continue to use the old approach, as they don’t understand the reasons for the change and are not confident in its rigour. This is another one of the many reasons to keep things simple and ‘auditable’. 4. How to supplement your attribution model? Depending on the nature of your marketing activity and your customer journey, you may be able to refine your attribution model with some additional data sources and analytical techniques, for example: Incrementality testing: for marketing activities which you cannot measure directly, but which you would expect to generate an immediate response from your target audience (e.g., Paid Social, Direct Mail, TV/Radio in some cases). The approach involves defining target segments who will receive the advertising and comparing the outcomes of the following days/weeks against a control group who were not. These groups are often defined geographically (e.g., only in the South West, or only in certain postcodes), but you can really use any criteria where you are confident in achieving a robust control group and where your tracking will allow you to analyse the results (e.g., don’t segment on postcodes if your customer journey doesn’t collect postcodes). You need to make sure your target audience is exposed enough to the advertising so be sure to allocate sufficient budget and narrow your target audience if necessary. Marketing mix modelling: an econometric modelling technique for larger advertisers and those who don’t ‘own’ the customer journey such as FMCG brands. This approach looks for correlations between marketing spend and growth in traffic/leads/conversions. This approach does not rely on understanding individual customer journeys so in my experience tends to produce an aggregated view of channel ROI rather than super granular results e.g., at keyword or creative level. One of the limitations to keep in mind with any attribution model is that it will skew towards short-term marketing activities which drive an immediate response – marketing mix modelling can offer a longer-term perspective. I’m planning to cover this in more depth in a future post, as it is something I’ve had less personal exposure to given the businesses I’ve worked with. Hold-out area: this may sound like a simple one, but if possible, I always try to keep a geographic area exposed to as little paid advertising as possible – perhaps a city/county/state. Like the incrementality test, it is a helpful way to understand the impact of organic channels such as SEO and word of mouth referrals. Primary research: asking your customers ‘how did you start the process of researching your purchase’ and ‘where did you first hear about us’ is a very worthwhile exercise. An attribution dataset can never capture the importance of word-of-mouth referrals and the reputation of your business. Don’t assume customers will have perfect recall, but qualitative interviews can be insightful alongside an attribution model. Across the many times I’ve run this type of research, for both B2B and B2C advertisers, word-of-mouth is invariably the single most common source of leads, accounting for up to 30% of purchases. Some conclusions In case you haven’t realised by now, building a robust attribution model is a never-ending process. There will always be ways you bring in additional datapoints, your marketing activities will be constantly changing, as will your customer journey. There are two principles that I try to keep in mind: 1. Attribution can never be perfect – it just must be good enough to make decisions each that will help you improve marketing efficiency. Given this, getting a first version of your model up and running then quickly iterating will create more value than spending months obsessing about a vastly complex data-driven attribution model, or joining every single touchpoint in a long enterprise sales journey together. 2. You are trying to understand human behaviour and your ability to influence it through data – so always ask yourself whether what you are seeing makes sense. All those journeys that start with a ‘Direct’ traffic source weren’t just people waking up one morning and spontaneously typing your website address into their browser. Make sure you can explain the human behaviour behind what your attribution data is telling you. There are several tech providers out there who can help with some aspects of building an attribution model – I appreciate that the approach set out here may seem quite technical or require access to development resource that you don’t have. However, understanding the analytical process end-to-end is still important so that you can see both the strengths and limitations of any tech solution you may consider. In a future post I’ll share some views on the providers that I’m aware of. Finally, if you need motivation to tackle your own attribution challenges, when we put in place an attribution model at CarTrawler we were able to reduce the Cost Per Acquisition in non-brand Paid Search by 60% at the same time as increasing volumes by 50% - as we could see which search terms and devices were driving the most valuable customers and use ROI to guide decision making for the first time. If you’d like to discuss how you can build a view of marketing attribution in your business, please Contact Me. All views expressed in this post are the author's own and should not be relied upon for any reason. Clearly.

‘Is the marketing function in your organisation a cost centre or a profit centre?’ This is a question I’ve asked many times, both as a consultant and an investor. It is shorthand for ‘do you understand the role of your marketing function in driving growth?’ and ‘how mature is your marketing function?’ Often, the answer to this question will be ‘a cost centre’. This is normally the case for SMEs and especially those selling to other businesses (B2B). I’m going to explain why you should aspire to have a marketing ‘profit centre’, how to assess your starting point and how to get there. Why should your marketing function be a profit centre? From the CEO’s perspective, a marketing function that operates as a profit centre offers some level of predictability of outputs rather than just outputs; for example, ‘delivering 1,000 customers at a £50 Cost Per Acquisition (CPA)’ vs. ‘spending a £50,000 marketing budget’. There should be an established understanding of cause and effect, some level of marketing attribution. A CEO will value having another controllable growth lever at their disposal. Being a profit centre means that the marketing function and Chief Marketing Officer (CMO) has both ownership and accountability for driving commercial outcomes. From the CEO’s perspective, this should lead to alignment between the marketing function and the overall business strategy, and between marketing spend and the overall financial objectives of the business. Together this should translate a higher chance of hitting collective goals than the alternative. From the CMO’s perspective, having clear ownership of commercial outputs, as opposed to just inputs, elevates their role significantly. In fact, this can be one of the main differences between organisations where the most senior marketer is part of the C-Suite team rather than one or two levels below, and responsible for spending a budget but not for quantified and specific commercial outputs. Being a profit centre also suggests that the organisation has collectively understood the value that a high performing and output-oriented marketing function can play in driving business growth, which should create more opportunities for career development for those in the marketing function and the CMO personally – for example being given responsibility for entering new international markets or taking ownership of broader strategic topics such as pricing or aspects of product and the overall customer journey. Finally, directly embedding a profit-based marketing output metric such as Return on Investment (ROI), perhaps based on Customer Lifetime Value (CLV/LTV), to gauge marketing effectiveness can be very helpful for a CMO. It provides a framework for deciding what to spend and how to spend it, as well as making conversations with the finance team more strategic and growth focused. How can you tell where you are starting from? The easiest way to work out whether your marketing function is operating as a cost centre or profit centre is to consider the recent conversations you’ve had about marketing spend. If the focus has been mostly on the amount you are spending, you are probably working with a cost centre. If the focus has been on the quantified results and ROI that than marketing function is generating, you are probably working with a profit centre. As a CMO, you could also try a simple exercise. If you went down the list of everything you spend on marketing, could you justify it based on a commercial return? Not just the presence of a particular channel but the specific amount being spent? Often when I’ve asked this, the main input to this year’s marketing budget is whatever last year’s budget was. You could also look at the KPIs used for the marketing function and the extent to which these are inputs vs outputs. Perhaps ask yourself my favourite question, "where would you spend your next £1"? How can you transition from marketing as a cost centre to a profit centre? There are five components which can help you move towards marketing as a profit centre over time: Start measuring marketing effectiveness - ROI (perhaps on LTV basis) or even CPA based on some level of marketing attribution; Use this understanding to start communicating internally who your most valuable customers are, and which marketing activities you would start/stop/continue with the aim of getting more of them; In your next planning cycle, armed with your new measurement of marketing effectiveness, adopt a zero-based approach to planning your marketing spend. In other words, forget what you’ve spent in the past and plan from scratch with the objective of generating as many profitable customers as possible. Make sure to talk to your finance team about this first though, as they will need to support your approach; In your internal conversations around this new approach to marketing planning, as well as more regular discussions of business performance, reframe conversations around ‘budget’ from talking about spend inputs to talking about commercial outputs; and Hire for people in your marketing function and as CMO who talk about quantified commercial outputs in their CVs vs awards and amount of budget previously managed. Some conclusions Working out whether a marketing function was operating as a cost centre vs. a profit centre is an important question or me when I’m reviewing potential investments. I’m looking for a demonstrable a growth lever that a Management team understand and can control, that will help to underpin my investment case. Of course, there are some situations when a marketing function may have something have a hybrid model. For example, I’ve worked with a restaurant operator where the marketing function operated as a ‘cost centre’ when it came to brand-level activity required to support each restaurant (e.g., creative content and point-of-sale material), but as a profit centre when operating the group’s well established and well-adopted customer loyalty programme. Even if you aren’t sure where your organisation sits today, I think that asking the question I started this post with will invariably lead to an interesting conversation about the role of marketing in your organisation, and hopefully some tangible outcomes. If you’d like to discuss the strategic role of the marketing function in your business, please Contact Me. All views expressed in this post are the author's own and should not be relied upon for any reason. Clearly.

Working as both a marketing leader and an investor, a constant challenge has been to prioritise among the myriad ways that I could spend my time. As Elton so wisely sang in Disney’s Lion King; there’s ‘more to do than can ever be done’. Among the many tools I've used to help me prioritise tasks, one that has proven to be invaluable is the Eisenhower Matrix, also known as the Urgent-Important matrix. This decision-making tool traces its origins to Dwight D. Eisenhower, the 34th U.S. President, who was revered for his exceptional organisational skills. Quoting an unknown academic, he said once "I have two kinds of problems, the urgent and the important. The urgent are not important, and the important are never urgent." This principle was further refined by Stephen Covey in his book ‘The 7 Habits Of Highly Effective People’ into the matrix we know today. I think this has specific utility for marketing leaders such as Chief Marketing Officers (CMOs) given the variety of different activities they can be pulled into during daily business. The Eisenhower Matrix can break down your to-do list into four quadrants: 1. Urgent and Important 2. Urgent but not Important 3. Not Urgent and Not Important 4. Important but not Urgent We’ll dive a bit deeper into each quadrant and I'll share some practical examples from my own experience as a marketing leader. Quadrant 1: Urgent and Important In this quadrant, we encounter tasks that require immediate attention and contribute significantly to our marketing goals. They often arise unexpectedly, leaving us little time to prepare. One instance from my own experience was when we had to manage a PR crisis that threatened to tarnish our brand's reputation significantly. It was all hands on deck, with our team working around the clock to navigate the crisis and mitigate potential damage. Similarly, launching a time-sensitive email campaign for a promotional event is another example of a task that falls into this quadrant. I remember when we would run our January sale at CarTrawler, this would become all-consuming for a week or so beforehand as we worked to make sure we had great content visible across the site. Quadrant 2: Urgent but not Important This quadrant is filled with tasks that demand immediate attention but don't substantially contribute to our overall marketing objectives. These are often routine tasks that, while necessary, can be time-consuming and divert attention from strategic goals. For instance, managing a barrage of non-critical emails that keep pinging throughout the day, or handling ad-hoc requests from other departments that could be addressed by someone else. One of my regular battles is with my overflowing inbox. While it's essential to stay connected, spending excessive time on non-essential correspondence can derail my focus from more important tasks. I try to manage this by dealing with emails at set times in the day, as well as delegating specific responsibilities where appropriate (such as representing the marketing team at cross-departmental meetings). Quadrant 3: Not Urgent and Not Important This quadrant comprises activities that neither require immediate attention nor contribute significantly to your marketing goals. These are tasks that tend to serve as distractions rather than value-adds. For instance, compiling reviews of competitor content without a specific question you are trying to answer, attending partner events with little return on investment, or getting stuck in unproductive internal meetings. It's a quadrant filled with tasks that create an illusion of busyness without contributing much to our goals. And finally, the quadrant that holds the most significance for me: Quadrant 4: Important but not Urgent These are tasks that, while not requiring immediate action, are critical for long-term success. It's often a strategic domain where the foundation for the future is laid. Consider planning and implementing your organic search/SEO strategy, for instance. This involves conducting market research, identifying emerging search trends and your Search Headroom, technical analysis of your site, and mapping out execution timelines. It's a process that doesn't scream for immediate attention on any given day but holds the key to future growth for many businesses. Significant analytical projects can also end up in the this quadrant - for example, measuring and increasing customer lifetime value isn't going to boost today's or this month's numbers; but almost certainly can influence performance over a multi-year time horizon. Innovation also often sits in this quadrant – running a test in a new channel, conducting an overhaul of your ad copy or creatives, developing new customer journeys. These tasks are easy to put off for a day but leave them for a year and you will start to lag your competitors and the needs of your target customers. Another task I've often placed here is nurturing relationships with key stakeholders. This includes clients, partners, and even team members. It involves regular check-ins, feedback sessions, and brainstorming collaborations—activities that contribute immensely to the team's growth and the brand's reputation in the long run. Understanding and cultivating the "Important but not Urgent" quadrant is a pivotal part of my role as a CMO. It's here that we prepare for the future, innovate, and build the backbone of your business’s longevity. This quadrant isn't about putting out fires; it's about fire prevention. Implementing the Eisenhower Matrix in the cadence of your marketing function offers more than just a structured to-do list; it provides a strategic roadmap that aligns your tasks with your long-term objectives. It serves as a reminder that effective leadership isn't born from merely reacting to problems but from proactive planning and focus on long-term goals. So next time you're overwhelmed with a list of tasks, pause, and categorise them in the Eisenhower Matrix. Remember, the 'Important but not Urgent' quadrant might seem quiet, but it's often where the seeds of your future success are sown.

LTV, or customer lifetime value, is one of the most important metrics for your business to understand. It equates to your ability to turn your proposition and customer relationships into monetary value over time. When combined with your Cost Per Acquisition (CPA), the LTV:CPA ratio demonstrates the fundamental unit economics of your business, and how efficiently you can grow. To help bring the theory of LTV and the LTV:CPA ratio to life, I wanted to take three real-world examples and offer some thoughts that may help you to interpret the data that is being presented. There is no standard methodology used, so knowing how to critique an individual example is important before you draw conclusions and/or make comparisons. This is something I've done many times when reviewing Information Memorandums and Management Presentations as an investor. I need to stress that the examples I’m going to share are only for information and education rather than for any kind of financial advice. I think it is also important to note that very few public companies disclose this type of information, and I think that all three examples should be praised for the fact that they do. They just happen to be good examples that help us to think about the questions we might ask the Management team if we want to better understand a specific LTV or LTV to CPA ratio analysis. Example 1 – Wix Wix, the website builder & CMS provider, presents a version of the LTV to CPA ratio called ‘Time to Return On (Marketing) Investment’. This looks at the cumulative ‘cohort bookings’ compared to marketing costs, citing a ratio of 4.8x after 19 quarters (4.25 years) based on the Q1 2018 cohort. On the face of it, this chart shows a positive trend – but that isn’t particularly meaningful, as it just relates to the allowing more time for a cohort to generate revenue before comparing against the customer acquisition costs. I would be asking to see a comparison between different cohorts over the same time period, e.g. the Q1 2022 cohort compared to the Q1 2021 cohort and the Q1 2020 cohort for the equivalent first three quarters of their relationship with Wix. This will tell us whether ROI is increasing, decreasing or is stable. Reading the small print, my interpretation is that the ‘cohort bookings’ is a revenue figure rather than a profit figure – I’m sure Wix has healthy software margins but I would want to look at a profit level ROI, having deducted direct costs as well as variable personnel costs such as their customer success team. The marketing costs are described as ‘direct acquisition marketing costs’. I would be asking whether this includes things like martech, agency and employee costs, as well as media costs that are most readily allocated to customer acquisition activities (vs. customer retention or partner marketing). Getting a true picture of Cost Per Acquisition (CPA) is in my experience the most overlooked aspect of analysing the LTV to CPA ratio. Example 2 – Vimeo Vimeo, the video hosting provider, goes a step further than Wix in showing the progression of its LTV to CPA (LTV/CAC) ratio over time, split between its two main customer segments. It uses this analysis to support the fact that it has ‘efficient [customer] acquisition’, which the ratios certainly support. Digging into the definition provided, a positive is that Vimeo is taking a gross margin level view of customer lifetime value – but I would be asking the Management team how they extrapolated value over the customer lifetime. I would guess that they’ve used a current measure of net revenue retention. I always treat this with caution as it implicitly assumes that the current rate of retention can be sustained indefinitely, which understates the risk of innovation and competitive disruption over the longer term. I prefer using a fixed time ‘window’ of time, for example five years. Looking at the timing of the big step-up in ROI through Covid for self-serve customers, it is possible that this was driven by a big positive change in net revenue retention which may or may not be sustained. As with the Wix example, I’d also be asking about the impact of variable personnel costs such as their customer success team, if these aren’t already factored into the gross margin. The customer acquisition costs here seem to encompass all sales and marketing costs, I would check that these include personnel costs and also ask whether we should be taking any costs out that are more related to servicing existing customers or partners – i.e. are we overstating Cost Per Acquisition? Example 3 - Remitly Remitly doesn’t show a trend over time for their LTV to CPA ratio, but they do use a fixed five-year window, which is the most common comparison period in my experience. Reading the small print, they say that they project future periods ‘based on robust statistical models that source thousands of existing customer observations’. This is a perfectly reasonable thing to do, but I would be asking how this might compare to the actual observed historical data as you may find that there has been a notable change in their proposition or customer behaviour which has led Remitly to adopt this projected approach to lifetime value. As with the above examples, I would also be digging into the definition of CPA here, which Remitly defines as ‘direct marketing expenses deployed to acquire new customers’. The same question applies around the inclusion of martech, agency and employee costs. One thing that I would also be curious to learn more about is the statement that there are ‘corridor-specific targets based on customer lifetime value’. This might shed some light on which corridors have the most potential for efficient growth, which would clearly be interesting to know. In conclusion – there are some common threads here. Make sure to read the small print to understand (i) the time period over which the LTV to CPA ratio is being calculated, (ii) whether lifetime value is based on profit (good) or revenue (bad), (iii) and how customer acquisition costs are being calculated. As I said above – these three examples are in the minority of public companies in sharing these metrics in the first place, and all look healthy. You are now hopefully armed with some questions that can be applied to any LTV to CPA ratio you see before you draw conclusions or make comparisons. If you’d like to discuss how you can better understand and use Customer Lifetime Value in your business, please Contact Me. All views expressed in this post are the author's own and should not be relied upon for any reason. Clearly.

How many times have you seen a set of customer ‘pen portraits’? The format is ubiquitous, normally a series of densely written pages of text accompanied with a stock image of someone smiling into the camera and a generic name, where we are told many specific, qualitative traits that our young professional or retired couple customer possesses – where they shop, their attitudes to politics, whether they have sugar in their coffee. The problem with this approach to customer segmentation in my experience is that it leaves me asking ‘so what?’. A robust customer segmentation needs to have three things: A ready comparison to available market demand – how much of the market does each segment account for in terms of volume/value size and growth; A ready comparison to our existing customer base, allowing us to define our market share of each segment in combination with (1) and define the segments that are worth the most to our business today & in the future (for example based on customer lifetime value; and ‘Prospectable’ characteristics that will allow marketers to take immediate actions to acquire customers from the most desirable segments. Pen portraits come from an analogue age when things like knowing which newspaper a prospective group of customers read was the most specific information a marketer could hope to know when deciding where to prospect for new customers. In today’s marketing world, a pen portrait can be helpful when thinking about the tone of voice of content or creative concepts, but it doesn’t really help inform where we should go looking for new customers. How to create a customer segmentation To create an actionable customer segmentation, you should focus on the ‘prospectable’ characteristics of your target customers. By this I mean the attributes or behaviours that are externally visible and therefore possible for you to target with your marketing activity. For example, in the B2B world this could include attributes such as company size, growth rate, vertical, or geographic location, and behaviours such as the recent appointment of a new CEO or an M&A transaction. In the B2C world this could include attributes such as where someone lives, or in some ages their age or gender, and behaviours such as the day/time they are searching for your product/service. We can’t really consider an attribute or behaviour as ‘prospectable’ unless it is something that we can target within our main marketing channels – can we bid more within performance marketing, or exclude prospects that don’t have these traits altogether? It is almost impossible to target based on traits such as attitudes – which often form a core part of pen portraits. If you’ve previously analysed customer lifetime value, this is normally a good place to start when building a segmentation, as it will tell you the traits that make the biggest difference to the value of a customer to your business. We want our segments to be unique and well differentiated from each other. The result of following this principle to building a customer segmentation is normally a much tighter definition of each customer segment perhaps with only 2 or 3 criteria used to define between 4 to 8 different segments. Because we are only using ‘prospectable’ traits, it is also normally a lot easier to map both the market and our existing customer base onto these segments, allowing us to map the areas of concentration and opportunity for our business. You can then use these segments in conjunction with primary research to understand things like awareness and consideration for your business by each segment. Figure 1 - example output from a customer segmentation exercise You can use various statistical techniques such as cluster analysis or more advanced machine learning-based models to use patterns in your own data to build segments, but as a starting point I always advocate starting with a segmentation that you define from first principles and then refine with more advanced analytics. Keeping it simple rarely leads you down the wrong path. This approach elevates a customer segmentation from a ‘marketing’ tool to a ‘strategic’ tool that can drive business planning and how you report performance. You might base your annual marketing plan on growing your share in a particularly attractive segment or decide to move away from a historically high-volume segment because you’ve realised it has lower overall customer lifetime value. Pen portraits can play a role when designing creative content but shouldn’t be the starting point for an actionable customer segmentation. If you’d like to discuss how you can create an actionable customer segmentation in your business, please Contact Me. All views expressed in this post are the author's own and should not be relied upon for any reason. Clearly.